| Abstract: |
| This talk is concerned with an data filling method based on mechanism learning, from the perspective of inverse problems. The underlying data mechanism, characterized by temporal-spatial or cross-sectional linear differential equations, is identified from observations on the known area and then exploited to infer that on the missing part, which improves interpretability and generalizability. Attention is paid to incorporation of prior information as higher order mechanism. Method analysis and numerical examples demonstrate effectiveness, robustness and flexibility of the method and it performs well over mechanism/scientific data, such as oceanic and atmospheric fields. |
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